Generating Equations for Mathematical Word Problems Using Multi-head Attention Transformer

Word problem-solving is challenging for students and artificial intelligence systems due to the need to convert natural language text into mathematical expressions. This paper proposes a unique method for generating mathematical equations for mathematical word problems using a Multi-Head Attention Transformer (MAT) model. The MAT model is a powerful tool that effectively processes sequential data by attending to relevant parts of the input sequence. Two datasets, namely the Math23K and the AI2 datasets, have been used to evaluate our approach against existing models. On the Math23K dataset, the implemented method achieves an accuracy of 60.74%, and on the mawps dataset, it achieves an accuracy of 80.0%, representing significant performance improvements over existing approaches. Our results demonstrate the effectiveness of the Multi-Head Attention Transformer model in generating mathematical equations for word problems, outperforming other models that use traditional sequence-to-sequence approaches. The analysis of the attention mechanism of our model is also mentioned, which sheds light on how it learns to attend to relevant parts of the input sequence to generate the correct mathematical expression. Overall, the implemented approach presents a promising solution for generating mathematical equations from word problems, with potential applications in education and other domains where accurate and efficient mathematical problem-solving is required.